Development of the matched filtration of a blurred digital image using its typical form

The object of this study is the aperture brightness of the image of the object, which has a variety of typical shapes in the frames of the series. It directly depends on the stability of the shooting conditions of the objects under study. Thus, determining the exact aperture brightness of an object in the frame becomes more difficult. For this purpose, a method was devised for determining the aperture brightness of an object using the typical shape of its image on a series of frames. This method is based on the formation of a typical shape of a digital image of an object based on data from all frames of the series. The typical shape makes it possible to take into account the peculiarities of the formation of the image of the object on each frame of the series. Based on this, a more accurate estimate of the initial approximation of the parameters of all Gaussian images of the object is performed. In addition, the adaptation of the method to the standard shape makes it possible to perform a more accurate assessment of the aperture brightness of the object in comparison with the analytically defined profile. An estimate of the aperture brightness of the object was derived using the least squares method. Due to minimization using the Levenberg-Marquardt algorithm, the use of the method improved identification with reference objects and reduced the number of false positives. The study showed a decrease in the standard deviation of frame identification errors by 5–7 times when using a typical shape of a digital image. The devised method for determining an object's aperture brightness using its image's typical shape was tested in practice within the framework of the CoLiTec project. It was implemented in the intra-frame processing unit of the CoLiTecVS software for the automated construction of brilliance curves of the studied variable stars. Owing to the use of the CoLiTecVS software and the proposed computational method implemented in it, more than 700,000 measurements of various objects under study were successfully processed and identified

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a method for determining the aperture brightness of an object using a typical shape of its image

Khlamov

Savanevych

Vlasenko

et al. 2023

“…The main disadvantage of this method is the fact that the method will not work correctly in the case when the typical image of the object on different frames of the series is different. An improved matched filtering method [22] using a standard image shape is also known. However, even it can only help filter the images of objects more accurately but not assess their position.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a method for determining the position of an object using a typical form of its image

Khlamov

Savanevych

Briukhovetskyi

et al. 2023

Violating the observation conditions for the investigated objects leads to the formation of diverse typical forms of objects throughout the frame in the series. As a consequence, determining the exact position of the object on the frame becomes difficult. To this end, a method was devised to determine the position of an object using the typical form of its image on a series of frames. This method is based on the formation of a typical form of a digital image of an object based on data from all frames of the series. This makes it possible to take into account the peculiarities of the very formation of the digital image of an object on each frame of the original series. Based on this, a more accurate assessment of the initial approximation of the parameters of all Gaussians of the object's image is performed. Adapting the method specifically for the typical form allows for a more accurate assessment of the positional parameters (coordinates) of the object in comparison with the analytically set profile. The estimation of the position of an object was obtained using the method of least squares. After that, minimization was performed using the Levenberg-Marquardt algorithm. Also, the use of the method makes it possible to improve identification with reference objects and reduce the number of false detections. The study showed a reduction in the standard deviation of frame identification errors by 7–10 times when using a typical digital image shape. The method devised for determining the position of an object using the typical form of its image was tested in practice within the framework of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software to automatically detect new objects and track known ones. Owing to the use of Lemur software and the proposed computational method implemented in it, more than 700,000 measurements of various objects under study were successfully processed and identified

“…The matched filtering procedure is also known [27,28], but it uses only an analytical image model. The disadvantage of this procedure is the inaccuracy of identification when the typical image of an object is different in different frames of the series.…”

Section: Introductionmentioning

confidence: 99%

Devising a procedure for the brightness alignment of astronomical frames background by a high frequency filtration to improve accuracy of the brightness estimation of objects

Vlasenko,

Khlamov,

Savanevych

2024

The object of this study is the background substrate of astronomical frames. To detect and compare the image of an object in a frame with its real image from astronomical catalogs, it is necessary to uniformly distribute the brightness of the background image substrate. Most often, the background alignment of astronomical frames is performed using the hardware calibration method applying the construction of service frames. However, it does not make it possible to eliminate the background from temporary stray light. Therefore, to solve this problem, a procedure has been proposed for brightness alignment of the background frame using high-pass filtering. For high-pass filtering of images, three high-pass filters were considered – an ideal filter, a Butterworth filter, and a Gaussian filter. To remove coarse-grained image components from the image, a high-pass filter was used, which attenuates low-frequency harmonics of the image spectrum while simultaneously passing high-frequency harmonics. Applying the devised procedure for brightness alignment of the background substrate of the frame has made it possible to increase the signal-to-noise ratio and reduce the dynamic range of the background substrate of the image. The study showed that when assessing brightness and identifying frames, the fitting provides better accuracy of reference to the starry sky. Also, the standard deviation of frame identification errors in this case is 5–7 times less than without using the devised procedure. The devised procedure for brightness alignment of the background frame substrate was tested in practice within the framework of the CoLiTec project. It was implemented at the stage of intra-frame processing in the Lemur software for automated detection of new objects and tracking of known objects